🟒 Prototyping ⭐
AI Infrastructure Updated January 17, 2025

Volition

Agentic frameworks that turn human intention into structured deliverables through linguistic optimization

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The Problem

Complex projects require repetitive prompt engineering and lack reusable, version-controlled agent templates that can compound and evolve

The Story Behind This App

Mission

Full project lifecycle agentic frameworks and prompt libraries that utilize volitional action of agents to provide structured outputs via deliverables or downstream field generation via sub-agents. Creates reusable templates to accomplish complex tasks that may compound and complete other complex tasks down the road.

The Volitional Deliverable F(x) Machine

The building block of the human-monitored Agent:

  1. User provides .yaml (user configured)
  2. YAML configures XML (prompt library provided)
    • Functions have detailed output specifications
  3. Runtime execution via deliverables export
  4. Deployment to user environments (manual/automatic)

Core Manifesto

  • Version Control First: Mutable, repeatable prompts from scratch with dynamic fields
  • Function-Like Interaction: Inputs beget outputs in a predictable, machine-like manner
  • Generalized Templates: Larger generalized templates over specialized ones
  • Work Breakdown Structure: Complex outputs reduced to simple, manageable deliverables
  • Omni-Channel Vision: Agents push to multiple environments seamlessly
  • Multi-Modal Approach: Vision, auditory, speech, visuals, touch integration
  • Tested Libraries: Version controlled prompt libraries over iterative MVPs
  • One-Click Orchestration: Streamlined execution over continual prompting

Linguistic Framework for Agent Design

The Human-as-Machine Model

Understanding data structures in LLMs through linguistic structures, optimized for human language and communication patterns backed by neuroscience and biomimicry.

1. Linguistic Components

  • Nouns (Entities/Objects): Deliverables, knowledge bases, scopes, WBS components
  • Verbs (Actions/Processes): Analyze, execute, refactor, optimize, deploy
  • Adjectives (Properties/Verticals): Technical, architectural, strategic
  • Adverbs (Modifiers): Iteratively, incrementally, collaboratively
  • Prepositions (Relationships): Within scope, across teams, through processes
  • Interrogatives (Questions): How, what, who, when, where, why

2. Brain-Computer Architecture Parallels

Volitional and Reflexive Synapses

Core Mappings:

  • Working memory ↔ RAM (volatile, immediate access)
  • Long-term memory ↔ Disk writes (persistent storage)
  • Visual processing ↔ Graphics pipeline (hierarchical extraction)
  • Motor cortex ↔ Action/Tool execution layer

Key Insight: Motor preparation as cache - the brain pre-computes movement patterns before execution, mapping directly to:

  • Function call preparation in LLMs
  • Tool-use priming through context
  • Pre-computed responses for common patterns

3. Project Architecture

Projects as organized memory and action structures:

  • WBS as Brain Repository: The things your brain composes to create volitional action
  • Configured Templates: Runtime-ready plans called β€œVolitions”
  • Cascading Runbooks: Volitions nesting into larger workflows
  • Dynamic Fields: Self-configuring parameters for future runtimes

Technical Implementation

Configuration Pipeline

# User-provided YAML
volition:
  name: "Project Scaffold"
  type: "development"
  outputs:
    - deliverable: "architecture_doc"
    - deliverable: "test_suite"
    - deliverable: "deployment_config"

XML Prompt Library

Detailed function specifications with:

  • Input parameters
  • Processing logic
  • Output schemas
  • Error handling
  • Multi-modal interfaces

Deliverable Export

  • Structured outputs to multiple environments
  • Version-controlled artifacts
  • Automated or manual deployment
  • Cross-platform compatibility

Use Cases

  1. Software Development: Complete project scaffolding with architecture, tests, and deployment
  2. Content Creation: Multi-format content generation with consistent voice
  3. Research Projects: Structured research outputs with citations and analysis
  4. Business Process: Automated workflow generation and optimization
  5. Educational Content: Curriculum development with assessments and materials

Integration Points

  • Version Control Systems: Git, SVN for prompt library management
  • CI/CD Pipelines: Automated deliverable deployment
  • Project Management Tools: JIRA, Asana integration for WBS
  • Documentation Systems: Automated doc generation
  • Multi-Modal Platforms: Voice, visual, and text interfaces

Future Vision

Volition aims to bridge the gap between human intention and machine execution, creating a seamless flow from thought to deliverable through linguistically-optimized, neuroscience-backed agent frameworks that understand and replicate human cognitive patterns in project execution.

Key Features

1. Volitional Deliverable F(x) Machine

What: YAML-configured XML runtime that transforms inputs into structured deliverables

Why: Creates predictable, machine-like outputs from human intentions

2. Linguistic Framework Engine

What: Maps human language patterns (nouns, verbs, adjectives) to agent actions

Why: Optimizes agent understanding through neuroscience-backed linguistic structures

3. Cascading Volitions

What: Nested agent workflows with dynamic field propagation

Why: Enables complex tasks to compound into larger automated processes

4. Multi-Modal Agent Interface

What: Supports vision, auditory, speech, visual, and touch modalities

Why: Allows agents to work across diverse input/output channels

5. Version-Controlled Prompt Libraries

What: Git-managed, tested prompt templates with semantic versioning

Why: Ensures repeatability and evolution of agent capabilities over time

User Journey

  1. 1 User defines project requirements in YAML configuration
  2. 2 System generates XML prompt from library templates
  3. 3 Agent executes volitional actions based on linguistic patterns
  4. 4 Deliverables are generated with detailed specifications
  5. 5 Outputs deploy to user environments (manual or automatic)
  6. 6 Results cascade to trigger downstream volitions if configured

Technical Architecture

Frontend

YAML configuration interface with visual workflow builder

Backend

Node.js runtime with XML parser and agent orchestration

Data

PostgreSQL for prompt libraries, Redis for runtime state

APIs

  • OpenAI API
  • Anthropic Claude
  • GitHub Actions
  • CI/CD webhooks

Hosting

Docker containers with Kubernetes orchestration

Moonshot Features (v2.0)

  • ☐ Brain-computer parallel processing with motor preparation caching
  • ☐ Self-evolving prompt libraries through usage analysis
  • ☐ Cross-project knowledge transfer between volitions
  • ☐ Real-time agent collaboration with conflict resolution

Market Research

Similar to: LangChain, AutoGPT, CrewAI, Microsoft AutoGen

Different because: Focuses on linguistic optimization and neuroscience-backed patterns rather than pure technical orchestration

Target users: Technical teams, project managers, and organizations seeking repeatable AI workflows

Open Questions

  • How to balance generalized vs specialized templates?
  • What's the optimal granularity for volition composition?
  • How to handle versioning conflicts in cascading workflows?
  • Can we achieve true one-click orchestration for enterprise workflows?

Resources & Inspiration

Discussion